Systems and methods for analyzing contact center interactions

ABSTRACT

Systems and methods for analyzing contact center interactions are provided. In this regard, a representative method includes: receiving information corresponding to an interaction between a contact center agent and a customer; and assessing quality of the interaction based, at least in part, on information corresponding to at least one of: a history of the customer; and an event corresponding to the customer and occurring subsequent to the interaction.

BACKGROUND

It is desirable in many situations to record communications, such astelephone calls. This is particularly so in a contact center in whichmany agents may be handling hundreds of telephone calls each every day.Recording of these telephone calls can allow for quality assessment ofagents, improvement of agent skills and/or dispute resolution, forexample.

In this regard, assessment of call quality is time consuming and verysubjective. For instance, a telephone call may last from a few secondsto a few hours and may be only one part of a customer interaction or mayinclude several independent interactions. The demeanor of the caller isalso influenced by events preceding the actual conversation—for example,the original reason for the call; the time spent waiting for the call tobe answered or the number of times the customer has had to call beforegetting through to the right person.

Assessing the “quality” of a telephone call is therefore difficult andsubject to error, even when done by an experienced supervisor orfull-time quality assessor. Typically, the assessment of a call isstructured according to a pre-defined set of criteria and sub-criteria.Some of these may relate to the initial greeting, the assessment of thereason for the call, the handling of the core reason for the call,confirming that the caller is satisfied with the handling of the call,and leaving the call.

Automation of the assessment process by provision of standardized formsand evaluation profiles have made such assessment more efficient, but itis still impractical to assess more than a tiny percentage of calls.Moreover, even with a structured evaluation form, different assessorswill evaluate a call differently with quite a wide variation of scores.Notably, this degree of inconsistency tends to produce haphazard scoringof agent performance, particularly when agents typically are scoredbased on a designated number of calls per month.

SUMMARY

In this regard, systems and methods for analyzing contact centerinteractions are provided. An exemplary embodiment of such a methodcomprises: receiving information corresponding to an interaction betweena contact center agent and a customer; and assessing quality of theinteraction based, at least in part, on information corresponding to atleast one of: a history of the customer; and an event corresponding tothe customer and occurring subsequent to the interaction.

Another exemplary embodiment of such a method comprises: determiningreview-candidate criteria used for designating which interaction amongmultiple interactions is to be reviewed; determining review-executioncriteria used for determining when an interaction, which is designatedfor review, can be reviewed; and responsive to identifying aninteraction of interest satisfying both the review-candidate criteriaand the review-execution criteria, providing access to informationcorresponding to the interaction of interest and informationcorresponding to a related interaction such that the interaction ofinterest can be reviewed in context.

An exemplary embodiment of such a system comprises a communicationanalyzer operative to an interaction analyzer operative to: receiveinformation corresponding to review-candidate criteria used fordesignating which interaction among multiple interactions is to bereviewed; receive information corresponding to review-execution criteriaused for determining when an interaction, which is designated forreview, can be reviewed; identify an interaction of interest satisfyingboth the review-candidate criteria and the review-execution criteria;and provide access to information corresponding to the interaction ofinterest and information corresponding to a related interaction suchthat the interaction of interest can be reviewed in context.

Other systems, methods, features and/or advantages of this disclosurewill be or become apparent to one with skill in the art upon examinationof the following drawings and detailed description. It is intended thatall such additional systems, methods, features, and advantages beincluded within this description and be within the scope of the presentdisclosure.

BRIEF DESCRIPTION

Many aspects of the disclosure can be better understood with referenceto the following drawings. The components in the drawings are notnecessarily to scale, emphasis instead being placed upon clearlyillustrating the principles of the present disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views. While several embodiments are described inconnection with these drawings, there is no intent to limit thedisclosure to the embodiments disclosed herein.

FIG. 1 is a schematic diagram illustrating an embodiment of a system foranalyzing contact center interactions.

FIG. 2 is a flowchart illustrating functionality (or methods steps) thatcan be preformed by the embodiment of the system for analyzing contactcenter interactions of FIG. 1.

FIG. 3 is a schematic representation of an exemplary interaction.

FIG. 4 is a flowchart illustrating functionality (or methods steps) thatcan be preformed by another embodiment of a system for analyzing contactcenter interactions.

FIG. 5 is a diagram illustrating an embodiment of a system for analyzingcontact center interactions that is implemented by a computer.

DETAILED DESCRIPTION

At least some of the aforementioned problems with scoring of calls areattributable to calls being reviewed in isolation. That is, whenreviewing a call, the reviewer typically has no awareness of thecustomer experience preceding the call. For instance, such a reviewer isunaware of how long the customer was waiting on hold, how many times thecustomer attempted to speak to an agent before being answered and/or howmany times the customer has called previously regarding the sameissue/order/interaction. Additionally, the reviewer oftentimes does notknow the outcome of the call. For instance, whether the call resulted ina sale, whether the customer's problem was resolved and/or whether thecustomer needs to call back again at a later time. Hence, when scoring acall in isolation, the reviewer can only assess the call from theperspective of how well the agent handled the call as a standalonescenario with little to no prior context and certainly no prescience.

In this regard, systems and methods for analyzing contact centerinteractions are provided. Specifically, several exemplary embodimentswill be described in which, in reviewing a call, at least one of theprevious history of the customer and subsequent actions corresponding tothe customer, e.g., outcome of the call, is considered. Thus, there isan opportunity for the assessment of the call to take into account andconsider other aspects of the customer experience, potentially resultingin more effective review.

By reviewing more than a single interaction, e.g., an entiretransaction, the reviewer can assess not only the agent's handling ofthe particular interaction but the overall businesses handling of thetransaction itself. By way of example, an agent may have done a greatjob at calming a customer down during a telephone call, but theirritation expressed by the customer may have been due to the companyshipping a faulty item. In this example, the agent may not requireadditional training, but those responsible for designing the item orensuring quality control may need to evaluate their respectiveprocesses. Thus, root cause of business issues can be identified inaddition to agent training needs/deficiencies.

Referring now in more detail to the drawings, FIG. 1 is a schematicdiagram illustrating an embodiment of a system for analyzing contactcenter interactions. As shown in FIG. 1, system 100 incorporates aninteraction analyzer 110 that is configured to enable assessment of aninteraction. In FIG. 1, the interaction is associated with acommunication session that is occurring between a caller 112 and anagent 114 via a communication network 116. In this embodiment, the agentis associated with a contact center that comprises numerous agents forinteracting with customers, e.g., caller 112.

One should note that network 116 can include one or more differentnetworks and/or types of networks. As a non-limiting, example,communications network 116 can include a Wide Area Network (WAN), theInternet, and/or a Local Area Network (LAN). Additionally, thecommunication analyzer can receive information corresponding to thecommunication session directly or from one or more various componentsthat are not illustrated in FIG. 1. By way of example, the informationcan be provided from a long term storage device that stores recordingsof the communication session, with the recordings being provided to thestorage device by a recorder. Additionally or alternatively, therecordings could be provided directly from such a recorder.

In operation, the analyzer of FIG. 1 performs various functions (ormethod steps) as depicted in the flowchart of FIG. 2. As shown in FIG.2, the functions include: receiving information corresponding to aninteraction between a contact center agent and a customer (depicted inblock 210); and enabling quality of the interaction to be assessedbased, at least in part, on information corresponding to at least oneof: a history of the customer; and an action corresponding to thecustomer and occurring subsequent to the interaction (depicted in block212). It should be noted that although the embodiment of FIG. 1 can givethe impression that all communications are telephone calls, variousother forms of communication, such as email and web-based chat, forexample, can be used.

FIG. 3A is a schematic representation of an exemplary interactioninvolving a customer and an agent. Specifically, the horizontal bar inFIG. 3A is a simplified timeline indicating events involved in acustomer interaction, with the timeline also graphically representingthe audio recording associated with that interaction. In this case, thecustomer contacted an agent for the purpose of renewing an automobileinsurance policy. Generally, the events depicted on the timelineinclude: a “start call” event, which begins an on hold period; an “agentreceives call” event, which ends the on hold period and which begins acommunication session; a “customer/agent communication session”; and an“end call” event, which terminates the interaction.

Other information available includes the start date and time of theinteraction, in this case, 10 Aug. 2006 at 10:05, timing marks(indicating that the on hold period was 5 minutes and that thecommunication session lasted for 17 minutes) and a note 1. Note 1indicates that more information was requested from the customer prior tocall termination.

All of the aforementioned information is available to a reviewer forreview of the interaction, including the audio recording. Notably, sucha review takes place after the call is terminated, as indicated by the“REVIEW” symbol. However, no other information associated with thecustomer and/or of related interactions is provided. Thus, thisinteraction is likely reviewed in isolation.

When so reviewed, it is likely that this interaction will be scored asaverage, i.e., not good and not bad. This is because there is verylimited information available upon which to base the score. Thus, therandom nature of selecting and reviewing interactions can be relativelyineffective.

In contrast, FIG. 3B is a schematic representation of exemplaryinteractions involving a customer and an agent. Specifically, suchinteractions and associated information (e.g., graphical and/or audibleinformation) can be provided to a user of an embodiment of a system foranalyzing contact center transactions. In this regard, such anembodiment enables information associated with an interaction (similarto the one depicted in FIG. 3A) to be reviewed along with other relevantinformation, thereby potentially resulting in more effective review.

In particular, FIG. 3B depicts three related interactions, in that eachinvolves the same customer. Specifically, information corresponding to afirst of the interactions includes: a “start call” event, which beginsan on hold period; an “agent receives call” event, which ends the onhold period and which begins a communication session; a “customer/agentcommunication session”; and an “end call” event, which terminates theinteraction. Other information includes the audio recording of thisinteraction, the start date and time of this interaction, in this case,10 Aug. 2006 at 10:05, timing marks (indicating that the on hold periodwas 5 minutes and that the communication session lasted for 17 minutes)and a note 1. Note 1 indicates that more information was requested fromthe customer prior to call termination.

With respect to the second interaction, information corresponding tothis interaction includes: a “start call” event, which begins an on holdperiod and an “end call” event, which ends the on hold period. Otherinformation includes the audio recording of this interaction, the startdate and time of this interaction, in this case, 12 Aug. 2006 at 12:010and timing marks (indicating that the on hold period was 10).

With respect to the third interaction, information corresponding to thisinteraction includes: a “start call” event, which begins an on holdperiod; an “agent receives call” event, which ends the on hold periodand which begins a communication session; a “customer/agentcommunication session”; and an “end call” event, which terminates theinteraction. Other information includes the audio recording of thisinteraction, the start date and time of this interaction, in this case,12 Aug. 2006 at 13:45, timing marks (indicating that the on hold periodwas 7 minutes and that the communication session lasted for 12 minutes)and notes 2 and 3. Note 2 indicates that the policy was renewed 6minutes into the communication session, and note 3 indicates thatadditional coverage was sold to the customer 11 minutes into thecommunication session.

All of the aforementioned information is available to a reviewer forreview of the first interaction, for example. Notably, such a reviewtakes place not only after the call associated with the firstinteraction has terminated, but after the complete transaction hasoccurred; in this case, after the automobile insurance policy has beenrenewed as indicated in the third interaction. Thus, informationassociated with the customer and of related interactions is providedduring review of the first interaction, thereby enabling the reviewer tounderstand and evaluate the agent based on the context within which theinteraction that is to be scored exists.

In scoring of the first interaction, the reviewer may become aware ofthe customer having terminated a call, i.e., the second interaction,after what the customer may have perceived to be a prolonged on holdtime. Additionally, despite the customer's potential frustration, theagent was able to convince the customer to purchase additional insurancecoverage instead of merely renewing the previous insurance policy.Clearly, knowledge of these related events could lead a reviewer toscore the agent more favorably.

In contrast, one can envision scenarios in which a random customer/agentinteraction results in a favorable outcome when viewed in isolation.However, such an interaction may have taken place after multipleattempts by the customer to resolve their issue. Clearly, having accessto these other related events, as can be accomplished using anembodiment for analyzing contact center interactions, can provide formore effective review.

In this regard, FIG. 4 is a flowchart depicting functionality of anembodiment of a system for analyzing contact center interactions. Asshown in FIG. 4, the functionality (or method) may be construed asbeginning at block 410, in which review candidate criteria (for decidingthose interactions that are to be reviewed) are determined. By way ofexample, review candidate criteria can include one or more ofinformation related to customer identification and the number ofinteractions required to complete a transaction. In block 412, reviewexecution criteria (for deciding when review of a candidate is to beperformed) are determined. For instance, review execution criteria caninclude one or more of an event and a waiting period.

In block 414, contacts are monitored and those contacts meeting thereview candidate criteria are identified (block 416). Notably, thecontacts can be monitored in real-time or can be monitored subsequent torecording. Then, in block 418, a determination is made as to whether thereview execution criteria have been satisfied with respect to anidentified candidate for review. If the review execution criteria havenot been satisfied, the process awaits satisfaction after which theprocess proceeds to block 420. In block 420, a determination is maderegarding whether additional related contact has been made prior toreview of the candidate. If such additional contact has been made, e.g.,the customer has called the agent again to discuss an issue, the processproceeds to block 422 in which review is delayed until satisfaction ofadditional review execution criteria. By way of example, such criteriacan include one or more of an event and a waiting period.

Once a determination is made in block 420 that no additional contactshave been made prior to review execution, the process proceeds to block424 in which information corresponding to the candidate contact, e.g.,an audio recording of the contact, is accessed for review. In block 426,information corresponding to one or more other contacts that are relatedto the candidate contact also is provided.

In some embodiments, audio recordings are associated with a particularcustomer and/or specific transaction by ANI/CLI. Note that this does notalways provide accurate correlation, but can be a reasonably effectiveindicator especially in consumer businesses in which customers call fromhome/cellphone. Additionally or alternatively, recordings can beassociated with a particular customer and/or specific transaction viaintegration with an Interactive Voice Response (IVR) and/or CustomerRelationship Management (CRM) system resulting in unambiguous customeridentification and/or transaction identification. In some embodiments,screen recordings of the agent's desktop during contact with a customeralso are correlated with the audio recordings.

In addition to calls where the customer is connected to an agent, insome embodiments, those calls in which the customer rings in (identifiedby ANI or entry into IVR system) but abandons the call before beinganswered are also presented as part of the overall “customer experience”to be reviewed. This allows the reviewer to understand and assess theimpact of the customer's experience prior to reaching the agent.

In some embodiments, recordings are selected at random for evaluationand/or on the basis of outcome of the call. By way of example, arecording may be selected based on how many subsequent calls are needed.In one example, calls may be deliberately selected according to thenumber of calls that the customer had to make in order to complete atransaction. That is, as more calls are required for a particulartransaction, the higher the probability is that the transaction will beselected for review. Consider, for example, renewal of a car insurancepolicy. Such a transaction should take a single call but in many cases,a second, third or even fourth call is made in relation to the samesubject. Each phone call costs the insurance provider money and causesthe customer to expend effort and hence become less satisfied with theservice the insurance company is providing. Therefore, as the number ofcalls for renewing an insurance policy exceeds one call, the transactionbecomes a more likely candidate for review.

In some embodiments, calls are not presented for analysis until apre-determined time has elapsed since the call and/or a pre-determinedevent has occurred. This delay allows the system to wait until the“outcome” of the call is known. Such delay can vary in durationaccording to the type of transaction to which the call related. Forexample, renewal of an annual insurance policy over the phone can oftenresult in a follow-up call when the new policy document arrives. Thismay, for example, show up an error in the data entered during the call.It may be appropriate, therefore, to wait for 1 week after the policydocument has been issued before considering the call that led to therenewal being presented for review. If one or more calls relating tothis policy are received in that period then they can combined with theoriginal-call and the set of calls are presented for review.

As an alternative to the time delay technique described above, an eventmay be treated as an adequate indication that the transaction has beencompleted successfully and that there is therefore no reason to wait anylonger before presenting the call(s) associated with that transactionfor review. By way of example, receipt of payment by post or the closureof a trouble ticket raised on a helpdesk can each be events indicatingthat the associated transactions are ready for review.

In some embodiments, regardless of whether a time delay technique or anevent driven technique is used, any follow-on contact may set a waitingperiod before review is undertaken. For instance, a call querying thenew policy could initiate a waiting period of a week, which is allowedto pass before the transaction is considered complete and presented forreview. Notably, contact from the customer via any medium (e.g., e-mail,web chat and browsing an associated website) may be identified and usedinitiate a waiting period. Such contact also could be presented as partof the total experience for review.

In some embodiments, the algorithm used to determine transactionboundaries may differ according to the type of call and/or customer. Byway of example, Gold card customers calls a specific number (DNIS) andselect one of 5 options from an automated attendant system e.g. renewal,new policy, policy detail change etc.

In some embodiments, more calls may be monitored as potential candidatesfor review (up to potentially all calls) and the number of follow-upcalls/contacts may be used as a criterion for selecting whichtransactions are to be reviewed. For example, 100% of calls leading to anew help desk ticket being created may be tracked. Of those for whichthe trouble ticket was closed by the end of this call, 1% may bepresented for review; 100% of these for which the same trouble ticketwas re-opened within a week are presented for review; 10% oftransactions resulting in 2 calls in the following week are presentedfor review; 50% of those resulting in five or more calls in thefollowing week are presented for review; and 100% of those for which thetrouble ticket is still open after two weeks are presented for review.

In some embodiments, the number of calls, the number of abandoned calls,the time spent waiting, the number of transfers, the call duration, thetime spent on hold, the number of transfers and/or other parameters maybe used as part of an overall weighted assessment of the “quality” ofthe interaction with the customer.

In some embodiments, cases in which a customer conducts multipletransactions during a single contact may be identified and highlightedfor review according to different rules than single transactioncontacts. For instance, a customer who renews their own and spouse'sinsurance policy in a single call can be highlighted for review based ona rule that reviews at least one such call per agent per month in orderto check that the agent is efficiently remembering relevant customerdetails and not having to ask for them again. Additionally oralternatively, a case in which a customer conducts multiple transactionsduring a single contact can be split into individual transactions forreview.

In some embodiments, a reviewer may update a previously enteredassessment as a result of a customer's later transaction. For instance,a previous transaction had been assessed as “very good” but the customernow says “Last time I ordered this, you sent me the wrong one . . . ”).

In some embodiments, in addition to the manual assessment of aproportion of transactions, the number of transactions and number ofcalls required to complete a transaction may be monitored and used as akey performance indicator and/or a component of an overall agentevaluation. For example, considering only the first call for eachtransaction, note the agent that took this call. If the transactionrequired no further calls, then give the agent a positive score. If thetransaction required one further call, give the agent a negative score;two further calls give them a more negative score (and the agent whotook the second segment a negative score too). Thus, a singletransaction can affect scoring of all of the agents involved in thetransaction, even if they worked on the transaction independently.

A similar approach can be used to derive a “first call resolution rate”for each agent. With enough calls being monitored, a statisticallysignificant “first call resolution rate” can be derived for each agenton each transaction type (or “skill”) that they handle. This can then becompared against the average for the call center, their team or peergroup. In some embodiments, the aggregated first call resolution ratesfor each skill/call type can be assessed and compared, with potentialproblem areas being highlighted.

In some embodiments, customers who consistently requiring multiple ormore than the average number of calls to complete a transaction can beidentified. This allows the reviewer to assess whether this customer isa “problem customer.” Such an identification can result in determiningthat such a customer is potentially unprofitable, and that it may beworthwhile reviewing whether or not to continue doing business with thatcustomer. Such an identification can also result in determining that thecustomer is experiencing a persistent or repetitive problem. By way ofexample, a courier company is unable to deliver to a location withinpromised time scales and hence a potential resolution can be identified.

In some embodiments, multiple call transactions can be compared to caseswhere their transactions are completed in single calls. In this way, itmay be possible to identify the difference between the successful “oneand done” approach and the others.

The interval between successive transactions or, where transactions areoverlapping or very frequent, the rate of transactions permonth/week/day can be used as a criterion for selecting whichtransactions should be reviewed. Additionally or alternatively, thevalue of the transactions may be used. For instance, the more valuablethe customer relationship is deemed to be, the more frequently the waythat customer is handled should be reviewed. Also, any change to thepattern, e.g. a sudden decrease in order frequency or value, should leadto the current and previous transaction(s) being presented for review.These may give clues to the reason for the change in behavior andsuggest ways in which the position can be recovered and/or preventedfrom happening again/to other customers.

Where transactions involve multiple individuals from an organization andthat individual can be determined (either from entry of their identityas part of the transaction process or by identifying that the speechcharacteristics of the speaker differ from those previously heard fromthat customer), selection of which transactions to review can bemodified to ensure fairness or deliberate bias. By way of example, thefirst transaction placed by any individual is reviewed, thereby makingsure that they have been entered into the CRM system properly, have beenmade to feel welcome, have had the ordering process explained to them,etc. As a further example, a higher proportion of transactions made byspecific individuals can be reviewed. For instance, contacts by the mostsenior purchaser are always reviewed.

FIG. 5 is a schematic diagram illustrating an embodiment of acommunication analyzer that is implemented by a computer. Generally, interms of hardware architecture, voice analyzer 500 includes a processor502, memory 504, and one or more input and/or output (I/O) devicesinterface(s) 506 that are communicatively coupled via a local interface508. The local interface 506 can include, for example but not limitedto, one or more buses or other wired or wireless connections. The localinterface may have additional elements, which are omitted forsimplicity, such as controllers, buffers (caches), drivers, repeaters,and receivers to enable communications.

Further, the local interface may include address, control, and/or dataconnections to enable appropriate communications among theaforementioned components. The processor may be a hardware device forexecuting software, particularly software stored in memory.

The memory can include any one or combination of volatile memoryelements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM,etc.)) and nonvolatile memory elements (e.g., ROM, hard drive, tape,CDROM, etc.). Moreover, the memory may incorporate electronic, magnetic,optical, and/or other types of storage media. Note that the memory canhave a distributed architecture, where various components are situatedremote from one another, but can be accessed by the processor.Additionally, the memory includes an operating system 510, as well asinstructions associated with an interaction analyzer 512, thefunctionality of exemplary embodiments of which are described above,e.g., the functionality described with respect to the flowchart of FIG.4.

It should be noted that embodiments of one or more of the systemsdescribed herein could be used to perform an aspect of speech analytics(i.e., the analysis of recorded speech or real-time speech), which canbe used to perform a variety of functions, such as automated callevaluation, call scoring, quality monitoring, quality assessment andcompliance/adherence. By way of example, speech analytics can be used tocompare a recorded interaction to a script (e.g., a script that theagent was to use during the interaction). In other words, speechanalytics can be used to measure how well agents adhere to scripts,identify which agents are “good” sales people and which ones needadditional training. As such, speech analytics can be used to findagents who do not adhere to scripts. Yet in another example, speechanalytics can measure script effectiveness, identify which scripts areeffective and which are not, and find, for example, the section of ascript that displeases or upsets customers (e.g., based on emotiondetection). As another example, compliance with various policies can bedetermined. Such may be in the case of, for example, the collectionsindustry where it is a highly regulated business and agents must abideby many rules. The speech analytics of the present disclosure mayidentify when agents are not adhering to their scripts and guidelines.This can potentially improve collection effectiveness and reducecorporate liability and risk.

In this regard, various types of recording components can be used tofacilitate speech analytics. Specifically, such recording components canperform one or more various functions such as receiving, capturing,intercepting and tapping of data. This can involve the use of activeand/or passive recording techniques, as well as the recording of voiceand/or screen data.

It should be noted that speech analytics can be used in conjunction withsuch screen data (e.g., screen data captured from an agent'sworkstation/PC) for evaluation, scoring, analysis, adherence andcompliance purposes, for example. Such integrated functionalitiesimprove the effectiveness and efficiency of, for example, qualityassurance programs. For example, the integrated function can helpcompanies to locate appropriate calls (and related screen interactions)for quality monitoring and evaluation. This type of “precision”monitoring improves the effectiveness and productivity of qualityassurance programs.

Another aspect that can be accomplished involves fraud detection. Inthis regard, various manners can be used to determine the identity of aparticular speaker. In some embodiments, speech analytics can be usedindependently and/or in combination with other techniques for performingfraud detection. Specifically, some embodiments can involveidentification of a speaker (e.g., a customer) and correlating thisidentification with other information to determine whether a fraudulentclaim for example is being made. If such potential fraud is identified,some embodiments can provide an alert. For example, the speech analyticsof the present disclosure may identify the emotions of callers. Theidentified emotions can be used in conjunction with identifying specificconcepts to help companies spot either agents or callers/customers whoare involved in fraudulent activities. Referring back to the collectionsexample outlined above, by using emotion and concept detection,companies can identify which customers are attempting to misleadcollectors into believing that they are going to pay. The earlier thecompany is aware of a problem account, the more recourse options theywill have. Thus, the speech analytics of the present disclosure canfunction as an early warning system to reduce losses.

Additionally, included in this disclosure are embodiments of integratedworkforce optimization platforms, as discussed in U.S. application Ser.No. 11/359,356, filed on Feb. 22, 2006, entitled “Systems and Methodsfor Workforce Optimization,” which is hereby incorporated by referencein its entirety. At least one embodiment of an integrated workforceoptimization platform integrates: (1) Quality Monitoring/CallRecording—voice of the customer; the complete customer experience acrossmultimedia touch points; (2) Workforce Management—strategic forecastingand scheduling that drives efficiency and adherence, aids in planning,and helps facilitate optimum staffing and service levels; (3)Performance Management—key performance indicators (KPIs) and scorecardsthat analyze and help identify synergies, opportunities and improvementareas; (4) e-Learning—training, new information and protocoldisseminated to staff, leveraging best practice customer interactionsand delivering learning to support development; and/or (5)Analytics—deliver insights from customer interactions to drive businessperformance. By way of example, the integrated workforce optimizationprocess and system can include planning and establishing goals—from bothan enterprise and center perspective—to ensure alignment and objectivesthat complement and support one another. Such planning may becomplemented with forecasting and scheduling of the workforce to ensureoptimum service levels. Recording and measuring performance may also beutilized, leveraging quality monitoring/call recording to assess servicequality and the customer experience.

One should note that the flowcharts included herein show thearchitecture, functionality, and/or operation of a possibleimplementation of software. In this regard, each block can beinterpreted to represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that in somealternative implementations, the functions noted in the blocks may occurout of the order. For example, two blocks shown in succession may infact be executed substantially concurrently or the blocks may sometimesbe executed in the reverse order, depending upon the functionalityinvolved.

One should note that any of the programs listed herein, which caninclude an ordered listing of executable instructions for implementinglogical functions (such as depicted in the flowcharts), can be embodiedin any computer-readable medium for use by or in connection with aninstruction execution system, apparatus, or device, such as acomputer-based system, processor-containing system, or other system thatcan fetch the instructions from the instruction execution system,apparatus, or device and execute the instructions. In the context ofthis document, a “computer-readable medium” can be any means that cancontain, store, communicate, propagate, or transport the program for useby or in connection with the instruction execution system, apparatus, ordevice. The computer readable medium can be, for example but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device. More specific examples (anonexhaustive list) of the computer-readable medium could include anelectrical connection (electronic) having one or more wires, a portablecomputer diskette (magnetic), a random access memory (RAM) (electronic),a read-only memory (ROM) (electronic), an erasable programmableread-only memory (EPROM or Flash memory) (electronic), an optical fiber(optical), and a portable compact disc read-only memory (CDROM)(optical). In addition, the scope of the certain embodiments of thisdisclosure can include embodying the functionality described in logicembodied in hardware or software-configured mediums.

It should be emphasized that the above-described embodiments are merelypossible examples of implementations. Many variations and modificationsmay be made to the above-described embodiments. All such modificationsand variations are intended to be included herein within the scope ofthis disclosure.

1. A method for analyzing contact center interactions comprising:determining review-candidate criteria used for designating whichinteraction among multiple interactions is to be reviewed; determiningreview-execution criteria used for determining when an interaction,which is designated for review, can be reviewed; responsive toidentifying an interaction of interest satisfying both thereview-candidate criteria and the review-execution criteria, providingaccess to information corresponding to the interaction of interest andinformation corresponding to a related interaction such that theinteraction of interest can be reviewed in context; and delaying reviewfor a predetermined time period responsive to determining that acustomer associated with the interaction of interest has attemptedanother interaction subsequent to the interaction of interest.
 2. Themethod of claim 1, wherein the review-candidate criteria includes apredetermined number of interactions for completing a transaction suchthat, responsive to determining that a customer has had at least thepredetermined number of interactions for completing a transaction, atleast one of those interactions is designated for review.
 3. The methodof claim 1, wherein the review-candidate criteria includes a customeridentification such that, responsive to determining that a particularcustomer is a party to an interaction, that interaction is designatedfor review.
 4. The method of claim 1, wherein the review-executioncriteria includes a specified event such that, responsive to determiningthat the specified event has occurred, the interaction designated forreview can be reviewed.
 5. The method of claim 1, wherein thereview-execution criteria includes a waiting period such that,responsive to determining that the waiting period has expired, theinteraction designated for review can be reviewed.
 6. The method ofclaim 1, further comprising: recording the interactions.
 7. The methodof claim 6, wherein, in recording the interactions, audio and screeninformation is recorded.
 8. The method of claim 1, further comprisingperforming automated evaluation of the interaction of interest.
 9. Themethod of claim 8, wherein performing automated evaluation comprisesperforming script adherence analysis.
 10. The method of claim 8, whereinperforming automated evaluation comprises evaluating the interaction ofinterest for fraud.
 11. The method of claim 1, wherein at least some ofthe interactions are facilitated using Internet Protocol packets. 12.The method of claim 11, monitoring the Internet Protocol packets toidentify interactions satisfying the review-candidate criteria and thereview-execution criteria.
 13. A method for analyzing contact centerinteractions comprising: receiving information corresponding to aninteraction between a contact center agent and a customer; assessingquality of the interaction based, at least in part, on informationcorresponding to at least one of: a history of the customer; and anevent corresponding to the customer and occurring subsequent to theinteraction; and delaying assessing for a predetermined time periodresponsive to determining that a customer associated with theinteraction has attempted another interaction subsequent to theinteraction that is to be assessed for quality.
 14. The method of claim13, wherein the information corresponding to a history of the customerincludes information corresponding to a previous interaction of thatcustomer.
 15. The method of claim 13, wherein the event is completion ofa transaction associated with the interaction.
 16. A system foranalyzing contact center interactions comprising: an interactionanalyzer operative to: receive information corresponding toreview-candidate criteria used for designating which interaction amongmultiple interactions is to be reviewed; receive informationcorresponding to review-execution criteria used for determining when aninteraction, which is designated for review, can be reviewed; identifyan interaction of interest satisfying both the review-candidate criteriaand the review-execution criteria; provide access to informationcorresponding to the interaction of interest and informationcorresponding to a related interaction such that the interaction ofinterest can be reviewed in context; and delay review for apredetermined time period responsive to determining that a customerassociated with the interaction of interest has attempted anotherinteraction subsequent to the interaction of interest.
 17. The system ofclaim 16, wherein the information corresponding to a related interactionincludes at least one of: a history of the customer; and an event,corresponding to the customer, occurring subsequent to the interaction.18. The system of claim 16, wherein the review-candidate criteriaincludes a predetermined number of interactions for completing atransaction such that, responsive to determining that a customer has hadat least the predetermined number of interactions for completing atransaction, at least one of those interactions is designated forreview.
 19. The system of claim 16, wherein the review-executioncriteria includes a waiting period such that, responsive to determiningthat the waiting period has expired, the interaction designated forreview can be reviewed.
 20. The system of claim 16, wherein theinteraction analyzer is further operative to monitor Internet Protocolpackets associated with the multiple interactions to identify thoseinteractions satisfying the review-candidate criteria and thereview-execution criteria.
 21. The system of claim 16, furthercomprising a computer, and wherein the interaction analyzer is embodiedas software executing on the computer.
 22. The system of claim 16,further comprising a recorder operative to record the interaction ofinterest.
 23. The system of claim 16, further comprising means forrecording the interaction of interest.